| Presentation | 2022-08-05 Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals Kazuya Sawada, Yutaka Shimada, Tohru Ikeguchi, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In this report, by modifying a nonlinear method of detecting causality, we propose a method of detecting causality for point processes, such as spike trains, based on nonlinear dynamical systems theory. We modified a previous method of detecting causality based on the accuracy of mutual prediction using information on attractors reconstructed from observed time series through the time-delay coordinate system by applying the possibility of reconstructing dynamical systems from the inter-spike intervals and by considering the firing times. We also used twin surrogate data for significance test of prediction accuracy. We applied the proposed method to spike trains of two neurons generated from a mathematical neuron model and investigated its effectiveness. As a result, we confirmed that the proposed method correctly detects causality when neurons are bidirectionally or unidirectionally coupled. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Nonlinear time series analysis / Causality / Point process / Spike train / Twin surrogates |
| Paper # | CCS2022-36 |
| Date of Issue | 2022-07-28 (CCS) |
| Conference Information | |
| Committee | IN / CCS |
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| Conference Date | 2022/8/4(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hokkaido University(Centennial Hall) |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Network Science, Future Network, Cloud/SDN/Virtualization, Contents Delivery/Contents Exchange, and others |
| Chair | Kunio Hato(Internet Multifeed) / Megumi Akai(Hokkaido Univ.) |
| Vice Chair | Tsutomu Murase(Nagoya Univ.) / Hidehiro Nakano(Tokyo City Univ.) / Masaki Aida(TMU) |
| Secretary | Tsutomu Murase(KDDI Research) / Hidehiro Nakano(Nagaoka Univ. of Tech.) / Masaki Aida(NTT) |
| Assistant | / Hiroyuki Yasuda(Univ. of Tokyo) / Hiroyasu Ando(Tsukuba Univ.) / Tomoyuki Sasaki(Shonan Inst. of Tech.) / Miki Kobayashi(Rissho Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Information Networks / Technical Committee on Complex Communication Sciences |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Detecting causality for spike trains based on reconstructing dynamical system from inter-spike intervals |
| Sub Title (in English) | |
| Keyword(1) | Nonlinear time series analysis |
| Keyword(2) | Causality |
| Keyword(3) | Point process |
| Keyword(4) | Spike train |
| Keyword(5) | Twin surrogates |
| 1st Author's Name | Kazuya Sawada |
| 1st Author's Affiliation | Tokyo University of Science(TUS) |
| 2nd Author's Name | Yutaka Shimada |
| 2nd Author's Affiliation | Saitama University(Saitama Univ.) |
| 3rd Author's Name | Tohru Ikeguchi |
| 3rd Author's Affiliation | Tokyo University of Science(TUS) |
| Date | 2022-08-05 |
| Paper # | CCS2022-36 |
| Volume (vol) | vol.122 |
| Number (no) | CCS-145 |
| Page | pp.pp.48-53(CCS), |
| #Pages | 6 |
| Date of Issue | 2022-07-28 (CCS) |